Development of a Ground-Based AI Vision System for Fine-Scale Weed Detection in Tropical Reforestation

Principal Investigator

Tropical reforestation initiatives face significant early-stage threats from fast-growing weed species that compete with young seedlings for sunlight, nutrients, and water. While drone-based monitoring provides valuable aerial perspectives, it often lacks the resolution needed to detect weeds growing in close proximity to seedlings or partially hidden by surrounding vegetation. To complement aerial detection, this project proposes the development of a ground-based computer vision system for fine-scale weed detection and growth dominance estimation, enabling precision weeding and decision support in reforestation programs. This first year of the TerraWeed project will focus on hardware setup, initial dataset creation, and the development of deep learning models for close-range classification and segmentation of weeds versus tree seedlings. This foundational work will provide a basis for deploying intelligent ground-based systems that support high-precision intervention in complex tropical environments.

09 Jan 2026 - 08 Jan 2027

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